Confirmatory factor analysis of the multicomponent mental health literacy measure with university student athletes and student trainers
Bibliographic record
Abstract
Mental Health (MH) research is well researched in Canadian university students; however, less popular in Canadian student athletes. Mental Health Literacy (MHL), the attitudes and beliefs supporting recognition, management and prevention of MH disorders, has recently been introduced into university athletics. The Multicomponent Mental Health Literacy Measure (MMHLM) (Jung et al., 2016), has shown good reliability and validity, however, not specifically within Canadian university athletics. As MHL is context dependent, it must be measured within different populations and settings. The current study will assess the factor structure of the MMHLM within a sample of Canadian university student athletes and trainers. 290 participants (81 male; 209 female; 241 student athletes; 42 student trainers) completed the scale via a secure on-line site. The MMHLM, a 26-item scale measures three MH factors: knowledge, beliefs, and resources. Responses are answered on a 5-point scale but scored dichotomously as the presence or absence of MHL. Data was subjected to a Confirmatory Factor Analysis. Generalized Least Squares extraction method was used and robust indices were interpreted. Results indicated that the model showed good fit to the data (CFI = 0.964; NNFI = -.960, RMSEA = 0.010); all items loaded significantly on their factor. All factors showed internal consistency alphas greater than 0.70. Comparative analysis of genders and roles were not attempted due to unbalanced ratios. The MMHLM appears to be a psychometrically sound measure, reflects the 3-factor representation of MHL, and is recommended for use in future studies on MH awareness in Canadian university sport.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".